CASE STUDIES · 86 PROJECTS
Real engineering problems. Built for production.
Real engineering problems, practical delivery, and scalable systems built for production.
- 3×FASTER PLATFORM · RELIAS
- ZeroMANUAL WARRANTY CHECKS · DELL
- 50+MODULES MAPPED · CLARITEV
SELECTED WORK
Problems we solved, and how.
Taking the manual work out of Dell's support operations
Dell's support operations, dispatch auditing, warranty validation and knowledge-base processing ran on legacy, manual workflows. Brandsmashers automated them end to end, from service-tag checks to dispatch audits, and turned product data into structured, API-ready information.
READ FULL CASE STUDY- Zeromanual warranty checks
- 1,000sproducts processed
- Real-timeAPI-ready data
- CASE STUDY · EDTECH MODERNISATION
A 3× faster learning platform for Relias' learners
Relias needed its learning platform to scale, with a faster backend, smarter automation and a modern, responsive frontend. Brandsmashers rebuilt backend services, added a custom rule engine for automated validations, and delivered a responsive Angular UI.
- 3×faster platform
- 100%validations automated
- Any deviceresponsive UI
- CASE STUDY · LEGACY DISCOVERY
How deep reverse engineering helped Claritev build a confident path to modernization
Claritev was transforming one of its core applications into a new, modern model but the existing system had grown over years, with some areas documented and others understood only through the code itself. Brandsmashers was brought in to build one complete, unified picture of the system so Claritev’s BA team could define the new model with confidence.
- 50+legacy modules reverse-engineered
- End-to-endblueprint for planning
- Less reworkacross BA, Dev and QA
- CASE STUDY · AI PRODUCT BUILD
Saagas.AI: an ethical AI platform for licensed creator styles
Saagas.AI wanted creators to use licensed influencer styles with full consent, identity protection and legal compliance. Brandsmashers built consent-driven onboarding with identity verification, AI models trained only on approved styles, and revenue-sharing dashboards.
- Consent-firstby design
- Fair payoutsfor influencers
- Multi-formatready
- CASE STUDY · MOBILE PAYMENTS
How Brandsmashers helped Airtel Africa deliver clearer, safer Airtel Money transactions
Airtel Africa was enhancing its mobile app to offer a smoother, more reliable digital payment experience. As part of the iOS development team, Brandsmashers contributed directly to the Airtel Money section building the UI screens and transaction flows that customers rely on every time they move money.
Read full case study - CASE STUDY · FINANCIAL SERVICES
How custom Java packages helped an asset management firm reduce build failures and cut dependency risk
A financial services and asset management firm was running a fleet of Java microservices that kept breaking in the same place: dependencies. Brandsmashers embedded a Java engineer into the team, untangled the dependency tree, and built a custom package layer that every service could safely share.
Read full case study - CASE STUDY · AGENTIC AI
From ticket queues to resolution: a multi-agent support system for an online marketplace
A fast-growing marketplace needed support that resolves problems, not just answers questions. Brandsmashers designed a supervisor-led team of AI agents that works across order, payment, logistics and help desk systems, with guardrails and a human always within reach.
- 50–60%routine requests resolved (target)
- < 1 minfirst response (target)
- 6 agentsone supervisor, five specialists
- CASE STUDY · AGENTIC AI
An orchestrated personal concierge for a travel platform
A digital-first travel platform wanted to move from a search-and-book catalogue to a guided experience that stays with the traveller before, during and after a trip. Brandsmashers designed a concierge agent that plans, remembers consented preferences and handles disruptions before travellers have to call.
- 6 agentsone concierge, five specialists
- Proactivedisruption handling
- Consent-firsttraveller memory
- CASE STUDY · AGENTIC AI
Agentic AI development & orchestration: AI agents that move from conversation to action
Most enterprises already have AI assistants that answer questions. The next step is AI that executes controlled business workflows. This scenario models an agentic procurement workflow built around a measurable process, with humans still approving exceptions and high-value purchases.
- 2,880modeled AI-eligible requests a month
- 8,000procurement requests a month
- CASE STUDY · AI SECURITY
AI agent security & governance: securing AI agents before they become business risks
AI agents can interpret instructions, call tools and trigger actions across systems. This scenario models an enterprise with 120 internal agents moving from simply deploying agents to an AI-agent governance architecture: inventory, identity, least privilege, auditability and human approval.
- 120AI agents governed
- <10target over-permissioned agents
- CASE STUDY · HEALTHCARE AI
Healthcare AI & clinical workflow automation: less documentation, same clinical judgment
Physicians spend significant time documenting encounters and completing administrative tasks. This scenario models an AI-assisted documentation workflow for a 35-physician organisation, where AI assists and the clinician always reviews and approves the final record.
- ~163 hrsmodeled time released a week
- ~33%modeled documentation reduction
BRANDSMASHERS TECHNOLOGY CAPABILITY
Build. Scale. Modernize.
Across these case studies, the common requirement is not simply more technology. It is the right combination of capabilities, at the right time.
- AI engineering
- Software development
- Cloud
- Cybersecurity
- DevOps
- Data
- Domain expertise
- 01IT staff augmentation
- 02Dedicated development teams
- 03AI/ML engineering
- 04Full-stack development
- 05Cloud engineering
- 06DevOps & SRE
- 07Cybersecurity engineering
- 08Data engineering
- 09Application modernization
- 10QA & automation
- 11API & system integration
Whether you need a specialised engineer, a complete development pod or extra capacity for an existing technology team, the engagement is built around your delivery model.
- A specialised engineer
- A complete development pod
- Extra capacity for your team
Technology teams need more than talent. They need the right engineering capability at the right time.
TALK TO BRANDSMASHERSYOUR TURN
Your problem could be our next case study.
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